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Sabine Filoni 's recent article nails half the attribution problem — hotels are paying Google and Meta for customers they already won through their own marketing. Last-click attribution has turned into an expensive shell game, and the industry keeps playing. But there's a bigger issue hiding in plain sight, and it's costing hotels even more than wasted ad spend. Here's what I mean: most hotels measure marketing success at the reservation and move on. But that booking isn't r

Your revenue manager just spent four hours building a report that's already outdated. Not because they're slow. Because pulling data from your PMS, reconciling it with your POS, cross-referencing your CRM, and synthesizing it into something actionable takes four hours. Minimum. By the time they're done, the window to act has closed. Your systems work perfectly. The data is pristine. The problem isn't collection — it's the wasteland between "we have the data" and "we know what

Watch What It Does. The AI community is tied up in knots over whether AI has "judgment." Wrong question. Here's what happened last week: A hotel GM was facing ownership with a year-over-year decline in room nights. Not the conversation you want to have. LaderaAI didn't just dump data on them. It dug through the noise and found the story: Direct sales up. Corporate and group business stronger. ADR increases that more than offset the occupancy dip. It positioned the presentati

Aditya Agarwal built Facebook's first search engine. Spent years as Dropbox's CTO. Then he used AI for a weekend and felt "profound sadness" for all the years he'd lost to manual work. Over the next five days, he shipped more code than in the previous five years. He noticed something else. When hiring for AI-native roles, traditional credentials predicted nothing. Years of experience? Worthless. What predicted success? Personal projects. Side builds. Evidence of someone who

Not because they're not performing. Because the data that proves it is trapped in systems nobody connects. The sales manager whose guests spend 40% more on F&B. The revenue strategy that cuts cancellations 15 points below portfolio average. The marketing campaign with a higher cost per booking—and dramatically higher revenue per stay. All happening. None visible. Here's what that costs you: Excellence you can't see can't be celebrated. Can't be rewarded. Can't be repeated. An

The acquisition closes. Ownership wants portfolio-level data by Friday. But the new property runs a different PMS, has a different OTA mix, and none of it maps to what you already see across your other hotels. Every property you add is another data silo you're responsible for — and the hidden cost of growth is the time your team spends trying to translate them. There's a better way.

A roundup from Hotel Management put it plainly: 'AI does not hide dysfunction.' The industry is investing heavily in AI for operations — pricing, housekeeping, scheduling. But what about upstream? Most hotel marketing teams are spending millions on acquisition with no visibility into which campaigns bring guests who cancel, and which bring guests who spend three times more at the restaurant. That's not a small oversight. That's six-figure budget decisions made with half the d

U.S. hotel TrevPAR dropped 8.8% last year while room rates held steady — meaning guests are spending less once they arrive. The industry's response is right: invest in F&B, spa, and experiences. But there's a problem nobody's talking about. If your marketing team is still optimizing for cost-per-booking, they have no idea which campaigns bring guests who actually spend money on property.

Gartner predicted a 25% drop in traditional search volume by 2026. We're here. ChatGPT, Perplexity, and Google AI Overviews are now sending travelers to hotel websites — and that traffic is measurable. But here's what nobody can answer yet: do those guests actually show up? Do they cancel? Do they spend at the spa or the bar? An entirely new discovery channel is growing, and we're still only measuring the click.

For decades, enterprise-level data analytics was a luxury only Marriott and Hilton could afford — armies of data scientists, massive infrastructure, years of investment. Mid-market operators made decisions based on gut feel and hoped for the best. That era is over. Today, a management company with 8 properties can access the same analytical firepower as the giants — in seconds, not weeks.